ReX: Causal Responsibility Explanations for image classifiers

ReX is a causal explainability tool for image classifiers. ReX is black-box, that is, agnostic to the internal structure of the classifier. We assume that we can modify the inputs and send them to the classifier, observing the output. ReX provides sufficient, minimal single explanations, non-contiguous explanations (for partially obscured images), multiple explanations and contrastive explanations (sufficient, necessary and minimal).

ReX organisation

For more information and links to the papers, see the background page.

Installation

Clone this repository and cd into it.

git clone git@github.com:ReX-XAI/ReX.git
cd ReX/

We recommend creating a virtual environment to install ReX. ReX has been tested using versions of Python >= 3.10. The following instructions assume conda:

conda create -n rex python=3.12
conda activate rex
pip install .

This should install an executable rex in your path.

Note:

By default, onnxruntime will be installed. If you wish to use a GPU, you should uninstall onnxruntime and install onnxruntime-gpu instead. You can alternatively edit the pyproject.toml to read “onnxruntime-gpu >= 1.17.0” rather than “onnxruntime >= 1.17.0”.

Quickstart

ReX requires as input an image and a model. ReX natively understands onnx files. Train or download a model (e.g. Resnet50) and, from this directory, run:

rex tests/test_data/dog.jpg --model resnet50-v1-7.onnx -vv --output dog_exp.jpg

To view an interactive plot for the responsibility map, run::

rex tests/test_data/dog.jpg --model resnet50-v1-7.onnx -vv --surface

To save the extracted explanation to a file:

rex tests/test_data/dog.jpg --model resnet50-v1-7.onnx --output dog_exp.jpg

ReX also works with PyTorch, but you will need to write some custom code to provide ReX with the prediction function and model shape, as well as preprocess the input data. See the sample scripts in tests/scripts/.

rex tests/test_data/dog.jpg --script tests/scripts/pytorch_resnet50.py -vv --output dog_exp.jpg

Other options:

# with spatial search rather than the default global search
rex <path_to_image> --model <path_to_model> --strategy spatial

# to run multiple explanations
rex <path_to_image> --model <path_to_model> --multi

# to view a responsibility landscape heatmap
rex <path_to_image> --model <path_to_model>  --heatmap

# to save a responsibility landscape surface plot
rex <path_to_image> --model <path_to_model>  --surface <path_and_extension>

ReX configuration is mainly handled via a config file; some options can also be set on the command line. ReX looks for the config file rex.toml in the current working directory and then $HOME/.config/rex.toml on unix-like systems.

If you want to use a custom location, use:

rex <path_to_image> --model <path_to_model> --config <path_to_config>

An example config file is included in the repo as example.rex.toml. Rename this to rex.toml if you wish to use it.

Command line usage

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Explaining AI through causal Responsibility EXplanations

usage: ReX [-h] [--output [OUTPUT]] [-c CONFIG] [--processed]
           [--script SCRIPT] [-v] [-q] [--surface [SURFACE]]
           [--heatmap [HEATMAP]] [--model MODEL] [--strategy STRATEGY]
           [--database DATABASE] [--multi [MULTI]]
           [--contrastive [CONTRASTIVE]] [--iters ITERS] [--analyze]
           [--analyse] [--mode MODE] [--spectral] [--version]
           filename

Positional Arguments

filename

file to be processed, either an image, a numpy array, a mat file, or a nifti file

Named Arguments

--output

show minimal, sufficient causal explanation, optionally saved to <OUTPUT>. Requires a PIL compatible file extension

-c, --config

optional config file to use for ReX

--processed

prevent ReX from performing any preprocessing

Default: False

--script

custom loading and preprocessing script, mostly for use with pytorch models

-v, --verbose

increase verbosity level, either -v or -vv

Default: 1

-q, --quiet

set verbosity level to 0 (errors only), overrides –verbose

Default: False

--surface

surface plot of the responsibility map, optionally saved to <SURFACE>

--heatmap

heatmap plot of the responsibility map, optionally saved to <HEATMAP>

--model

model in .onnx format

--strategy, -s

explanation strategy, one of < multi | spatial | global | spotlight >, defaults to global

--database, -db

store output in sqlite database <DATABASE>, creating db if necessary. Please include the extension in the filename

--multi

multiple explanations, with optional number <x> of spotlights, defaults to value in <rex.toml>, or 10 if undefined

--contrastive

a contrastive explanation, minimal, necessary and sufficient. Needs optional number <x> of floodlights, defaults to value in <rex.toml>, or 10 if undefined

--iters

manually override the number of iterations set in the config file

--analyze

area, entropy and (possibly) insertion/deletion curves

Default: False

--analyse

area, entropy and (possibly) insertion/deletion curves

Default: False

--mode, -m

assist ReX with your input type, one of <tabular>, <spectral>, <RGB>, <voxel>, <audio>

--spectral

set ReX input type to <spectral>, shortcut for –mode spectral

Default: False

--version

show program’s version number and exit

Examples

Explanation

An explanation for a ladybird. This explanation was produced with 20 iterations, using the default masking colour (0). The minimal, sufficient explanation itself is pretty printed using the settings in [rex.visual] in rex.toml.

ladybird responsibility map minimal explanation

Setting raw = true in rex.toml produces the image which was actually classified by the model.

ladybird raw

Multiple Explanations

rex tests/test_data/peacock.jpg --model resnet50-v1-7.onnx --strategy multi --output peacock.png

The number of explanations found depends on the model and some of the settings in rex.toml.

peacock peacock 1 peacock 2 peacock 3

Occluded Images

occluded bus

occluded_bus_rm

bus_explanation

Explanation Quality

rex tests/test_data/ladybird.jpg --script tests/scripts/pytorch_resnet50.py --analyse

INFO:ReX:area 0.000399, entropy difference 6.751189, insertion curve 0.964960, deletion curve 0.046096

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